Application of stellar photometry to the analysis of microarray images

Improvements in the analysis of microarray images are critical for accurately quantifying gene expression levels. The acquisition of accurate spot intensities directly influences the results and interpretation of statistical analyses. This dissertation discusses the implementation of a novel approach to the analysis of cDNA microarray images. We use a stellar photometric model, the Moffat function, to quantify microarray spots from nylon microarray images. The inherent flexibility of the Moffat shape model makes it ideal for quantifying microarray spots. We apply our novel approach to a Wilms' tumor microarray study and compare our results with a fixed-circle segmentation approach for spot quantification. Our results suggest that different spot feature extraction methods can have an impact on the ability of statistical methods to identify differentially expressed genes. We also used the Moffat function to simulate a series of microarray images under various experimental conditions. These simulations were used to validate the performance of various statistical methods for identifying differentially expressed genes. Our simulation results indicate that tests taking into account the dependency between mean spot intensity and variance estimation, such as the smoothened t-test, can better identify differentially expressed genes, especially when the number of replicates and mean fold change are low. The analysis of the simulations also showed that overall, a rank sum test (Mann-Whitney) performed well at identifying differentially expressed genes. Previous work has suggested the strengths of nonparametric approaches for identifying differentially expressed genes. We also show that multivariate approaches, such as hierarchical and k-means cluster analysis along with principal components analysis, are only effective at classifying samples when replicate numbers and mean fold change are high. Finally, we show how our stellar shape model approach can be extended to the analysis of 2D-gel images by adapting the Moffat function to take into account the elliptical nature of spots in such images. Our results indicate that stellar shape models offer a previously unexplored approach for the quantification of 2D-gel spots. ^

G870A polymorphism of cyclin D1 gene influences cancer risk - From epidemiological study to mechanism analysis

Alternate splicing of the cyclin D1 gene gives rise to transcript a and b which encode two protein isoforms cyclin D1a and cyclin D1b. Through testing transcript a and transcript b in a series of human samples, we found that cyclin D1 transcript b is ubiquitously expressed as transcript a but in the lower abundance compared to transcript a. Epidemiological studies have reported that the cyclin D1 gene (CCND1) G870A polymorphism influences the risk for a variety of cancer. In this investigation, we examined the cyclin D1b levels in tumor samples with different genotypes and found that higher levels of cyclin D1b are expressed from the A allele than the G allele. Cyclin D1 is known as a cell cycle regulator facilitating the progression of the cell cycle from G1 to S phase in response to the mitogenic signals. It also interacts with several transcription factors and transcriptional coregulators to modulate their activities. It has been reported that cyclin D1a can substitute for estrogen to activate estrogen receptor α (ERα) mediated transcription and can induce the proliferation of estrogen responsive tissues. However the biological role of cyclin D1b in ERα transcriptional regulation has not been previously explored. In this study, we determined that cyclin D1b antagonizes the action of cyclin D1a on ERα mediated transcription. Cell proliferation assays provided the evidence that cyclin D1b negatively regulates estrogen responsive breast cancer cell growth. Taken together, our findings show that the CCND1 G870A polymorphism is correlated with increased levels of cyclin D1b and that cyclin D1b antagonizes the action of cyclin D1a on ERα mediated transcription providing evidence for the mechanism by which the CCND1 G870A polymorphism may be protective in certain types of breast cancer. ^

Bayesian inference and its application to meta-analysis of epidemiologic data

In Part One, the foundations of Bayesian inference are reviewed, and the technicalities of the Bayesian method are illustrated. Part Two applies the Bayesian meta-analysis program, the Confidence Profile Method (CPM), to clinical trial data and evaluates the merits of using Bayesian meta-analysis for overviews of clinical trials.^ The Bayesian method of meta-analysis produced similar results to the classical results because of the large sample size, along with the input of a non-preferential prior probability distribution. These results were anticipated through explanations in Part One of the mechanics of the Bayesian approach. ^

THE USE OF MULTIVARIATE ANALYSIS OF COVARIANCE IN EVALUATING THE COMPARABILITY OF LABORATORY DETERMINATIONS IN COOPERATIVE STUDIES

The role of clinical chemistry has traditionally been to evaluate acutely ill or hospitalized patients. Traditional statistical methods have serious drawbacks in that they use univariate techniques. To demonstrate alternative methodology, a multivariate analysis of covariance model was developed and applied to the data from the Cooperative Study of Sickle Cell Disease.^ The purpose of developing the model for the laboratory data from the CSSCD was to evaluate the comparability of the results from the different clinics. Several variables were incorporated into the model in order to control for possible differences among the clinics that might confound any real laboratory differences.^ Differences for LDH, alkaline phosphatase and SGOT were identified which will necessitate adjustments by clinic whenever these data are used. In addition, aberrant clinic values for LDH, creatinine and BUN were also identified.^ The use of any statistical technique including multivariate analysis without thoughtful consideration may lead to spurious conclusions that may not be corrected for some time, if ever. However, the advantages of multivariate analysis far outweigh its potential problems. If its use increases as it should, the applicability to the analysis of laboratory data in prospective patient monitoring, quality control programs, and interpretation of data from cooperative studies could well have a major impact on the health and well being of a large number of individuals. ^